Overview

Dataset statistics

Number of variables14
Number of observations243
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory26.7 KiB
Average record size in memory112.5 B

Variable types

Numeric11
Categorical2
DateTime1

Alerts

Temperature is highly correlated with RH and 6 other fieldsHigh correlation
RH is highly correlated with Temperature and 5 other fieldsHigh correlation
Rain is highly correlated with Temperature and 2 other fieldsHigh correlation
FFMC is highly correlated with Temperature and 7 other fieldsHigh correlation
DMC is highly correlated with df_index and 6 other fieldsHigh correlation
DC is highly correlated with df_index and 5 other fieldsHigh correlation
ISI is highly correlated with Temperature and 7 other fieldsHigh correlation
BUI is highly correlated with df_index and 6 other fieldsHigh correlation
FWI is highly correlated with df_index and 8 other fieldsHigh correlation
df_index is highly correlated with DMC and 4 other fieldsHigh correlation
Ws is highly correlated with Temperature and 1 other fieldsHigh correlation
Classes is highly correlated with df_index and 8 other fieldsHigh correlation
Region is highly correlated with RHHigh correlation
df_index is uniformly distributed Uniform
df_index has unique values Unique
Rain has 133 (54.7%) zeros Zeros
ISI has 4 (1.6%) zeros Zeros
FWI has 9 (3.7%) zeros Zeros

Reproduction

Analysis started2022-11-18 15:59:47.835820
Analysis finished2022-11-18 15:59:54.610599
Duration6.77 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct243
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121
Minimum0
Maximum242
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2022-11-18T21:29:54.648060image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12.1
Q160.5
median121
Q3181.5
95-th percentile229.9
Maximum242
Range242
Interquartile range (IQR)121

Descriptive statistics

Standard deviation70.29224708
Coefficient of variation (CV)0.5809276618
Kurtosis-1.2
Mean121
Median Absolute Deviation (MAD)61
Skewness0
Sum29403
Variance4941
MonotonicityNot monotonic
2022-11-18T21:29:54.695334image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
0.4%
1811
 
0.4%
1541
 
0.4%
1551
 
0.4%
1561
 
0.4%
1571
 
0.4%
1581
 
0.4%
1591
 
0.4%
1601
 
0.4%
1611
 
0.4%
Other values (233)233
95.9%
ValueCountFrequency (%)
01
0.4%
11
0.4%
21
0.4%
31
0.4%
41
0.4%
51
0.4%
61
0.4%
71
0.4%
81
0.4%
91
0.4%
ValueCountFrequency (%)
2421
0.4%
2411
0.4%
2401
0.4%
2391
0.4%
2381
0.4%
2371
0.4%
2361
0.4%
2351
0.4%
2341
0.4%
2331
0.4%

Temperature
Real number (ℝ≥0)

HIGH CORRELATION

Distinct19
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.15226337
Minimum22
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2022-11-18T21:29:54.734949image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile26
Q130
median32
Q335
95-th percentile37.9
Maximum42
Range20
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.628039481
Coefficient of variation (CV)0.1128393183
Kurtosis-0.1414144576
Mean32.15226337
Median Absolute Deviation (MAD)3
Skewness-0.1913273285
Sum7813
Variance13.16267048
MonotonicityNot monotonic
2022-11-18T21:29:54.769583image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3529
11.9%
3125
10.3%
3424
9.9%
3323
9.5%
3022
9.1%
3221
8.6%
3621
8.6%
2918
7.4%
2815
6.2%
278
 
3.3%
Other values (9)37
15.2%
ValueCountFrequency (%)
222
 
0.8%
243
 
1.2%
256
 
2.5%
265
 
2.1%
278
 
3.3%
2815
6.2%
2918
7.4%
3022
9.1%
3125
10.3%
3221
8.6%
ValueCountFrequency (%)
421
 
0.4%
403
 
1.2%
396
 
2.5%
383
 
1.2%
378
 
3.3%
3621
8.6%
3529
11.9%
3424
9.9%
3323
9.5%
3221
8.6%

RH
Real number (ℝ≥0)

HIGH CORRELATION

Distinct62
Distinct (%)25.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.04115226
Minimum21
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2022-11-18T21:29:54.813252image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile37
Q152.5
median63
Q373.5
95-th percentile86
Maximum90
Range69
Interquartile range (IQR)21

Descriptive statistics

Standard deviation14.82816012
Coefficient of variation (CV)0.2390052341
Kurtosis-0.5089428114
Mean62.04115226
Median Absolute Deviation (MAD)11
Skewness-0.2427904556
Sum15076
Variance219.8743326
MonotonicityNot monotonic
2022-11-18T21:29:54.858603image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6410
 
4.1%
5510
 
4.1%
588
 
3.3%
788
 
3.3%
548
 
3.3%
807
 
2.9%
687
 
2.9%
657
 
2.9%
737
 
2.9%
667
 
2.9%
Other values (52)164
67.5%
ValueCountFrequency (%)
211
 
0.4%
241
 
0.4%
261
 
0.4%
291
 
0.4%
311
 
0.4%
332
0.8%
343
1.2%
351
 
0.4%
361
 
0.4%
373
1.2%
ValueCountFrequency (%)
901
 
0.4%
893
1.2%
883
1.2%
874
1.6%
863
1.2%
842
 
0.8%
831
 
0.4%
823
1.2%
816
2.5%
807
2.9%

Ws
Real number (ℝ≥0)

HIGH CORRELATION

Distinct18
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.49382716
Minimum6
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2022-11-18T21:29:54.899386image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11
Q114
median15
Q317
95-th percentile20
Maximum29
Range23
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.811385309
Coefficient of variation (CV)0.1814519602
Kurtosis2.621703531
Mean15.49382716
Median Absolute Deviation (MAD)2
Skewness0.5555858445
Sum3765
Variance7.903887358
MonotonicityNot monotonic
2022-11-18T21:29:54.934811image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1443
17.7%
1540
16.5%
1330
12.3%
1728
11.5%
1627
11.1%
1825
10.3%
1915
 
6.2%
218
 
3.3%
127
 
2.9%
117
 
2.9%
Other values (8)13
 
5.3%
ValueCountFrequency (%)
61
 
0.4%
81
 
0.4%
92
 
0.8%
103
 
1.2%
117
 
2.9%
127
 
2.9%
1330
12.3%
1443
17.7%
1540
16.5%
1627
11.1%
ValueCountFrequency (%)
291
 
0.4%
261
 
0.4%
222
 
0.8%
218
 
3.3%
202
 
0.8%
1915
 
6.2%
1825
10.3%
1728
11.5%
1627
11.1%
1540
16.5%

Rain
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct39
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.762962963
Minimum0
Maximum16.8
Zeros133
Zeros (%)54.7%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2022-11-18T21:29:54.973725image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.5
95-th percentile4.37
Maximum16.8
Range16.8
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation2.003206796
Coefficient of variation (CV)2.625562305
Kurtosis25.82298667
Mean0.762962963
Median Absolute Deviation (MAD)0
Skewness4.568629806
Sum185.4
Variance4.012837466
MonotonicityNot monotonic
2022-11-18T21:29:55.014806image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0133
54.7%
0.118
 
7.4%
0.211
 
4.5%
0.310
 
4.1%
0.48
 
3.3%
0.76
 
2.5%
0.66
 
2.5%
0.55
 
2.1%
23
 
1.2%
1.13
 
1.2%
Other values (29)40
 
16.5%
ValueCountFrequency (%)
0133
54.7%
0.118
 
7.4%
0.211
 
4.5%
0.310
 
4.1%
0.48
 
3.3%
0.55
 
2.1%
0.66
 
2.5%
0.76
 
2.5%
0.82
 
0.8%
0.91
 
0.4%
ValueCountFrequency (%)
16.81
0.4%
13.11
0.4%
10.11
0.4%
8.71
0.4%
8.31
0.4%
7.21
0.4%
6.51
0.4%
61
0.4%
5.81
0.4%
4.71
0.4%

FFMC
Real number (ℝ≥0)

HIGH CORRELATION

Distinct173
Distinct (%)71.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.84238683
Minimum28.6
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2022-11-18T21:29:55.060109image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum28.6
5-th percentile47.13
Q171.85
median83.3
Q388.3
95-th percentile92.19
Maximum96
Range67.4
Interquartile range (IQR)16.45

Descriptive statistics

Standard deviation14.34964126
Coefficient of variation (CV)0.1843422567
Kurtosis1.040086992
Mean77.84238683
Median Absolute Deviation (MAD)5.8
Skewness-1.320130116
Sum18915.7
Variance205.9122042
MonotonicityNot monotonic
2022-11-18T21:29:55.102909image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88.97
 
2.9%
89.45
 
2.1%
89.14
 
1.6%
89.34
 
1.6%
85.44
 
1.6%
47.43
 
1.2%
88.33
 
1.2%
79.93
 
1.2%
78.33
 
1.2%
873
 
1.2%
Other values (163)204
84.0%
ValueCountFrequency (%)
28.61
0.4%
30.51
0.4%
36.11
0.4%
37.31
0.4%
37.91
0.4%
40.91
0.4%
41.11
0.4%
42.61
0.4%
44.91
0.4%
451
0.4%
ValueCountFrequency (%)
961
0.4%
94.31
0.4%
94.21
0.4%
93.92
0.8%
93.81
0.4%
93.71
0.4%
93.31
0.4%
931
0.4%
92.52
0.8%
92.22
0.8%

DMC
Real number (ℝ≥0)

HIGH CORRELATION

Distinct165
Distinct (%)67.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.68065844
Minimum0.7
Maximum65.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2022-11-18T21:29:55.148981image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.7
5-th percentile1.9
Q15.8
median11.3
Q320.8
95-th percentile41.04
Maximum65.9
Range65.2
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.39303975
Coefficient of variation (CV)0.8441746537
Kurtosis2.462550971
Mean14.68065844
Median Absolute Deviation (MAD)6.9
Skewness1.522982931
Sum3567.4
Variance153.5874343
MonotonicityNot monotonic
2022-11-18T21:29:55.193581image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.95
 
2.1%
12.54
 
1.6%
1.94
 
1.6%
2.53
 
1.2%
2.63
 
1.2%
163
 
1.2%
3.43
 
1.2%
16.53
 
1.2%
8.33
 
1.2%
33
 
1.2%
Other values (155)209
86.0%
ValueCountFrequency (%)
0.71
 
0.4%
0.92
0.8%
1.12
0.8%
1.21
 
0.4%
1.33
1.2%
1.71
 
0.4%
1.94
1.6%
2.11
 
0.4%
2.22
0.8%
2.41
 
0.4%
ValueCountFrequency (%)
65.91
0.4%
61.31
0.4%
56.31
0.4%
54.21
0.4%
51.31
0.4%
50.21
0.4%
471
0.4%
46.61
0.4%
46.11
0.4%
45.61
0.4%

DC
Real number (ℝ≥0)

HIGH CORRELATION

Distinct197
Distinct (%)81.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.4308642
Minimum6.9
Maximum220.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2022-11-18T21:29:55.241735image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum6.9
5-th percentile7.6
Q112.35
median33.1
Q369.1
95-th percentile158.94
Maximum220.4
Range213.5
Interquartile range (IQR)56.75

Descriptive statistics

Standard deviation47.66560598
Coefficient of variation (CV)0.9642883401
Kurtosis1.596466845
Mean49.4308642
Median Absolute Deviation (MAD)23.9
Skewness1.473460229
Sum12011.7
Variance2272.009994
MonotonicityNot monotonic
2022-11-18T21:29:55.286071image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85
 
2.1%
8.24
 
1.6%
7.54
 
1.6%
8.44
 
1.6%
8.34
 
1.6%
7.84
 
1.6%
7.64
 
1.6%
173
 
1.2%
9.12
 
0.8%
34.52
 
0.8%
Other values (187)207
85.2%
ValueCountFrequency (%)
6.91
 
0.4%
72
0.8%
7.11
 
0.4%
7.32
0.8%
7.42
0.8%
7.54
1.6%
7.64
1.6%
7.72
0.8%
7.84
1.6%
7.91
 
0.4%
ValueCountFrequency (%)
220.41
0.4%
210.41
0.4%
200.21
0.4%
190.61
0.4%
181.31
0.4%
180.41
0.4%
177.31
0.4%
171.31
0.4%
168.21
0.4%
167.21
0.4%

ISI
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct106
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.742386831
Minimum0
Maximum19
Zeros4
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2022-11-18T21:29:55.330678image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q11.4
median3.5
Q37.25
95-th percentile13.38
Maximum19
Range19
Interquartile range (IQR)5.85

Descriptive statistics

Standard deviation4.154233833
Coefficient of variation (CV)0.8759795397
Kurtosis0.86232522
Mean4.742386831
Median Absolute Deviation (MAD)2.4
Skewness1.140242565
Sum1152.4
Variance17.25765874
MonotonicityNot monotonic
2022-11-18T21:29:55.375961image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.18
 
3.3%
1.27
 
2.9%
0.45
 
2.1%
2.85
 
2.1%
4.75
 
2.1%
15
 
2.1%
1.55
 
2.1%
5.25
 
2.1%
5.65
 
2.1%
1.34
 
1.6%
Other values (96)189
77.8%
ValueCountFrequency (%)
04
1.6%
0.14
1.6%
0.24
1.6%
0.33
1.2%
0.45
2.1%
0.52
 
0.8%
0.64
1.6%
0.74
1.6%
0.83
1.2%
0.92
 
0.8%
ValueCountFrequency (%)
191
0.4%
18.51
0.4%
17.21
0.4%
16.61
0.4%
161
0.4%
15.72
0.8%
15.51
0.4%
14.31
0.4%
14.21
0.4%
13.82
0.8%

BUI
Real number (ℝ≥0)

HIGH CORRELATION

Distinct173
Distinct (%)71.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.69053498
Minimum1.1
Maximum68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2022-11-18T21:29:55.424104image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile2.42
Q16
median12.4
Q322.65
95-th percentile46.4
Maximum68
Range66.9
Interquartile range (IQR)16.65

Descriptive statistics

Standard deviation14.22842113
Coefficient of variation (CV)0.852484426
Kurtosis1.956016644
Mean16.69053498
Median Absolute Deviation (MAD)7.3
Skewness1.452744841
Sum4055.8
Variance202.4479679
MonotonicityNot monotonic
2022-11-18T21:29:55.469309image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35
 
2.1%
5.14
 
1.6%
7.73
 
1.2%
2.43
 
1.2%
2.93
 
1.2%
8.33
 
1.2%
14.23
 
1.2%
4.43
 
1.2%
11.53
 
1.2%
3.93
 
1.2%
Other values (163)210
86.4%
ValueCountFrequency (%)
1.11
 
0.4%
1.42
0.8%
1.62
0.8%
1.72
0.8%
1.82
0.8%
2.21
 
0.4%
2.43
1.2%
2.62
0.8%
2.72
0.8%
2.82
0.8%
ValueCountFrequency (%)
681
0.4%
67.41
0.4%
641
0.4%
62.91
0.4%
59.51
0.4%
59.31
0.4%
57.11
0.4%
54.91
0.4%
54.71
0.4%
50.91
0.4%

FWI
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct125
Distinct (%)51.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.035390947
Minimum0
Maximum31.1
Zeros9
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2022-11-18T21:29:55.517600image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q10.7
median4.2
Q311.45
95-th percentile21.53
Maximum31.1
Range31.1
Interquartile range (IQR)10.75

Descriptive statistics

Standard deviation7.440567726
Coefficient of variation (CV)1.057591225
Kurtosis0.654985265
Mean7.035390947
Median Absolute Deviation (MAD)3.8
Skewness1.147592511
Sum1709.6
Variance55.36204809
MonotonicityNot monotonic
2022-11-18T21:29:55.561107image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.412
 
4.9%
0.810
 
4.1%
0.59
 
3.7%
0.19
 
3.7%
09
 
3.7%
0.38
 
3.3%
0.97
 
2.9%
0.26
 
2.5%
0.75
 
2.1%
0.64
 
1.6%
Other values (115)164
67.5%
ValueCountFrequency (%)
09
3.7%
0.19
3.7%
0.26
2.5%
0.38
3.3%
0.412
4.9%
0.59
3.7%
0.64
 
1.6%
0.75
2.1%
0.810
4.1%
0.97
2.9%
ValueCountFrequency (%)
31.11
0.4%
30.31
0.4%
30.21
0.4%
301
0.4%
26.91
0.4%
26.31
0.4%
26.11
0.4%
25.41
0.4%
24.51
0.4%
241
0.4%

Classes
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
fire
137 
not fire
106 

Length

Max length8
Median length4
Mean length5.744855967
Min length4

Characters and Unicode

Total characters1396
Distinct characters8
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot fire
2nd rownot fire
3rd rownot fire
4th rownot fire
5th rownot fire

Common Values

ValueCountFrequency (%)
fire137
56.4%
not fire106
43.6%

Length

2022-11-18T21:29:55.604022image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-18T21:29:55.647203image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
fire243
69.6%
not106
30.4%

Most occurring characters

ValueCountFrequency (%)
f243
17.4%
i243
17.4%
r243
17.4%
e243
17.4%
n106
7.6%
o106
7.6%
t106
7.6%
106
7.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1290
92.4%
Space Separator106
 
7.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f243
18.8%
i243
18.8%
r243
18.8%
e243
18.8%
n106
8.2%
o106
8.2%
t106
8.2%
Space Separator
ValueCountFrequency (%)
106
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1290
92.4%
Common106
 
7.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
f243
18.8%
i243
18.8%
r243
18.8%
e243
18.8%
n106
8.2%
o106
8.2%
t106
8.2%
Common
ValueCountFrequency (%)
106
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1396
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f243
17.4%
i243
17.4%
r243
17.4%
e243
17.4%
n106
7.6%
o106
7.6%
t106
7.6%
106
7.6%

Region
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
Bejaia
122 
Sidi-Bel Abbes
121 

Length

Max length14
Median length6
Mean length9.983539095
Min length6

Characters and Unicode

Total characters2426
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSidi-Bel Abbes
2nd rowBejaia
3rd rowBejaia
4th rowSidi-Bel Abbes
5th rowBejaia

Common Values

ValueCountFrequency (%)
Bejaia122
50.2%
Sidi-Bel Abbes121
49.8%

Length

2022-11-18T21:29:55.678921image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-18T21:29:55.714606image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
bejaia122
33.5%
sidi-bel121
33.2%
abbes121
33.2%

Most occurring characters

ValueCountFrequency (%)
e364
15.0%
i364
15.0%
a244
10.1%
B243
10.0%
b242
10.0%
j122
 
5.0%
S121
 
5.0%
d121
 
5.0%
-121
 
5.0%
l121
 
5.0%
Other values (3)363
15.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1699
70.0%
Uppercase Letter485
 
20.0%
Dash Punctuation121
 
5.0%
Space Separator121
 
5.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e364
21.4%
i364
21.4%
a244
14.4%
b242
14.2%
j122
 
7.2%
d121
 
7.1%
l121
 
7.1%
s121
 
7.1%
Uppercase Letter
ValueCountFrequency (%)
B243
50.1%
S121
24.9%
A121
24.9%
Dash Punctuation
ValueCountFrequency (%)
-121
100.0%
Space Separator
ValueCountFrequency (%)
121
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2184
90.0%
Common242
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e364
16.7%
i364
16.7%
a244
11.2%
B243
11.1%
b242
11.1%
j122
 
5.6%
S121
 
5.5%
d121
 
5.5%
l121
 
5.5%
A121
 
5.5%
Common
ValueCountFrequency (%)
-121
50.0%
121
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2426
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e364
15.0%
i364
15.0%
a244
10.1%
B243
10.0%
b242
10.0%
j122
 
5.0%
S121
 
5.0%
d121
 
5.0%
-121
 
5.0%
l121
 
5.0%
Other values (3)363
15.0%

date
Date

Distinct122
Distinct (%)50.2%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
Minimum2012-01-06 00:00:00
Maximum2012-12-09 00:00:00
2022-11-18T21:29:55.749816image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:55.794151image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2022-11-18T21:29:53.951249image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:49.673399image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:50.189923image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:50.709804image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:51.110186image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:51.491233image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:51.894039image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:52.400313image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:52.786825image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:53.156881image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:53.555220image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:53.985318image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:49.730084image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:50.226222image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:50.745066image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:51.143446image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:51.526460image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:51.929280image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:52.434194image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:52.818888image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:53.191764image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:53.590168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:54.022555image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:49.800762image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:50.266783image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:50.783802image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:51.181608image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:51.565535image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:51.967546image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:52.472248image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:52.855114image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:53.230898image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:53.630091image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:54.058060image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:49.880231image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:50.305725image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:50.820849image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:51.217147image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:51.602875image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:52.003329image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:52.507315image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:52.889441image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:53.267620image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:53.666932image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:54.091570image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:49.944589image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:50.341901image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:50.855603image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:51.249979image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:51.637921image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:52.037886image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:52.540932image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:52.921318image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:53.302386image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:53.701531image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:54.256515image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:49.980318image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:50.380837image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:50.892706image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:51.285636image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:51.675667image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:52.074805image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:52.577601image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:52.956381image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:53.340012image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:53.738468image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:54.291063image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:50.015430image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:50.524054image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:50.928446image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:51.319840image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:51.712450image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:52.223668image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:52.612836image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:52.990024image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:53.375799image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:53.774799image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:54.325962image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:50.049826image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:50.560863image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:50.964173image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:51.353844image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:51.748646image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:52.259264image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:52.647559image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:53.022974image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:53.411556image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:53.809958image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:54.358959image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:50.082003image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:50.595610image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:50.997500image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:51.385406image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:51.782047image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:52.292417image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:52.680427image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:53.054366image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:53.445499image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:53.842903image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:54.395473image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:50.118478image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:50.634760image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:51.034343image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:51.421420image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:51.819731image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:52.329319image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:52.716264image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:53.089371image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:53.482301image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:53.879843image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:54.430767image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:50.154225image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:50.672657image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:51.071856image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:51.457419image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:51.857101image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:52.365867image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:52.752041image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:53.124603image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:53.519553image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T21:29:53.916154image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-18T21:29:55.840443image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-18T21:29:55.908504image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-18T21:29:55.970176image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-18T21:29:56.027428image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-18T21:29:56.074366image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-18T21:29:54.492831image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-18T21:29:54.577084image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexTemperatureRHWsRainFFMCDMCDCISIBUIFWIClassesRegiondate
003271120.757.12.58.20.62.80.2not fireSidi-Bel Abbes2012-01-06
112957180.065.73.47.61.33.40.5not fireBejaia2012-01-06
222968191.059.92.58.61.12.90.4not fireBejaia2012-01-07
332858182.263.73.28.51.23.30.5not fireSidi-Bel Abbes2012-01-07
443645140.078.84.810.22.04.70.9not fireBejaia2012-01-08
553852140.078.34.410.52.04.40.8not fireSidi-Bel Abbes2012-01-08
662576177.246.01.37.50.21.80.1not fireBejaia2012-01-09
772986160.037.90.98.20.11.40.0not fireSidi-Bel Abbes2012-01-09
882961131.364.44.17.61.03.90.4not fireBejaia2012-02-06
993073134.055.72.77.80.62.90.2not fireSidi-Bel Abbes2012-02-06

Last rows

df_indexTemperatureRHWsRainFFMCDMCDCISIBUIFWIClassesRegiondate
2332333077211.858.51.98.41.12.40.3not fireBejaia2012-11-09
2342343073140.079.26.516.62.16.61.2not fireSidi-Bel Abbes2012-11-09
2352352758170.088.921.337.88.721.212.9fireSidi-Bel Abbes2012-12-06
2362362681190.084.013.861.44.817.77.1fireBejaia2012-12-06
2372373175130.175.17.927.71.59.20.9not fireBejaia2012-12-07
2382383644130.090.112.619.48.312.59.6fireSidi-Bel Abbes2012-12-07
2392393921170.493.018.441.515.518.418.8fireSidi-Bel Abbes2012-12-08
2402403551130.381.315.675.12.520.74.2not fireBejaia2012-12-08
2412412988130.071.02.616.61.23.70.5not fireBejaia2012-12-09
2422423172140.084.28.325.23.89.13.9fireSidi-Bel Abbes2012-12-09